关键词: 改进的局部切空间排列（ILTSA）, 人脸识别, 流形学习, 可泛化

Abstract: In the case of data sparse and data non-uniform distribution and data manifold with large curvature, the conventional local tangent space arrangement method can not reveal the manifold structure effectively. In this paper, a Generalized ILTSA（GILTSA）manifold learning is proposed. The method is based on the Improved Local Tangent Space Arrangement（ILTSA）algorithm. In solving the problem of manifold structure, it can not only obtain the low dimensional feature for face recognition, but also can deal with the problem of increasing data set. Firstly, on the basis of the nearest distance between the neighbor sets to select sample, the proposed method can get the low dimensional manifold of the training set to search for the nearest training sample set of each new sample. Then combined with ILTSA algorithm, it calculates low-dimensional manifolds according to its nearest sample projection distance. The experiments are tested on face database ORL, Swiss roll and the handwritten “2” images. The results show that the proposed GILTSA method increases the overall accuracy in comparison with correlated local linear embedding and local tangent space alignment algorithm.

Key words: Improved Local Tangent Space Alignment（ILTSA）, face recognition, manifold learning, generalization